from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.make_report()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.001	NaN	7.356	0.0	-1	1	NaN	0.050	0.004	0.217	0.218	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	NaN	7.339	0.0	-1	5	NaN	0.049	0.001	0.224	0.224	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.001	NaN	7.141	0.0	1	100	NaN	0.049	0.002	0.230	0.230	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.683	0.0	-1	100	NaN	0.049	0.001	0.244	0.244	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.787	0.0	1	5	NaN	0.050	0.001	0.234	0.234	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	NaN	6.618	0.0	1	1	NaN	0.048	0.001	0.251	0.251	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.361	0.0	-1	1	NaN	0.007	0.000	0.638	0.638	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.391	0.0	-1	5	NaN	0.007	0.001	0.568	0.569	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.400	0.0	1	100	NaN	0.007	0.001	0.535	0.536	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.414	0.0	-1	100	NaN	0.007	0.001	0.523	0.525	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.424	0.0	1	5	NaN	0.007	0.000	0.539	0.540	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	NaN	0.429	0.0	1	1	NaN	0.007	0.000	0.530	0.530	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.979	0.116	NaN	0.000	0.002	-1	1	0.663	0.402	0.012	4.928	4.931	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.003	NaN	0.000	0.021	-1	1	1.000	0.010	0.001	2.152	2.156	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.644	0.074	NaN	0.000	0.003	-1	5	0.757	0.403	0.017	6.563	6.569	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	NaN	0.000	0.023	-1	5	1.000	0.009	0.000	2.518	2.520	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.127	0.063	NaN	0.000	0.002	1	100	0.882	0.458	0.014	4.645	4.647	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	NaN	0.000	0.023	1	100	1.000	0.010	0.001	2.364	2.370	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.751	0.065	NaN	0.000	0.003	-1	100	0.882	0.480	0.016	5.736	5.739	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	NaN	0.000	0.023	-1	100	1.000	0.010	0.001	2.217	2.230	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.108	0.037	NaN	0.000	0.002	1	5	0.757	0.416	0.021	5.068	5.074	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	5	1.000	0.009	0.000	2.347	2.350	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.410	0.016	NaN	0.001	0.001	1	1	0.663	0.393	0.011	3.589	3.591	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	1	1.000	0.009	0.000	2.418	2.420	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.599	0.020	NaN	0.000	0.002	-1	1	0.896	0.089	0.002	18.013	18.019	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	NaN	0.000	0.004	-1	1	1.000	0.001	0.000	7.103	7.116	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.425	0.066	NaN	0.000	0.002	-1	5	0.922	0.090	0.003	26.890	26.903	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	NaN	0.000	0.006	-1	5	1.000	0.001	0.000	10.016	10.144	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.800	0.016	NaN	0.000	0.002	1	100	0.929	0.143	0.005	12.616	12.624	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	3.992	4.013	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.269	0.026	NaN	0.000	0.002	-1	100	0.929	0.143	0.004	15.862	15.867	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	8.474	8.505	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.770	0.017	NaN	0.000	0.002	1	5	0.922	0.088	0.002	20.014	20.018	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	5.301	5.322	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.093	0.017	NaN	0.000	0.001	1	1	0.896	0.089	0.003	12.320	12.325	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	3.055	3.075	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.355	0.099	NaN	0.034	0.0	-1	1	NaN	0.895	0.374	2.632	2.853	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.054	0.089	NaN	0.026	0.0	-1	5	NaN	0.694	0.027	4.399	4.403	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.374	0.120	NaN	0.024	0.0	1	100	NaN	0.752	0.011	4.489	4.489	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.043	0.061	NaN	0.026	0.0	-1	100	NaN	0.689	0.018	4.420	4.421	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.055	0.091	NaN	0.026	0.0	1	5	NaN	0.824	0.024	3.706	3.707	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.139	0.112	NaN	0.025	0.0	1	1	NaN	0.728	0.017	4.312	4.313	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.019	0.0	-1	1	NaN	0.004	0.002	0.236	0.267	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	-1	5	NaN	0.002	0.001	0.311	0.381	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.028	0.0	1	100	NaN	0.001	0.001	0.385	0.520	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.030	0.0	-1	100	NaN	0.001	0.000	0.480	0.487	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	1	5	NaN	0.001	0.000	0.587	0.588	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	1	NaN	0.001	0.000	0.522	0.523	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.805	1.139	NaN	0.000	0.001	-1	1	0.929	0.113	0.005	7.145	7.154	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	8.852	9.044	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.971	0.400	NaN	0.000	0.001	-1	5	0.946	0.185	0.006	5.238	5.241	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	6.903	7.125	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.202	0.717	NaN	0.000	0.005	1	100	0.951	0.604	0.024	8.609	8.616	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	2.678	2.767	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.073	0.383	NaN	0.000	0.003	-1	100	0.951	0.617	0.023	4.985	4.988	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	-1	100	1.000	0.001	0.000	4.683	4.833	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.627	0.364	NaN	0.000	0.002	1	5	0.946	0.215	0.006	7.553	7.557	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.001	0.000	2.845	2.908	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.797	0.333	NaN	0.000	0.001	1	1	0.929	0.111	0.002	7.155	7.156	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	3.281	3.390	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.020	NaN	0.000	0.000	-1	1	0.891	0.001	0.000	55.223	55.300	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	20.515	20.914	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.002	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	30.187	30.211	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	13.952	14.178	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.008	NaN	0.000	0.000	1	100	0.894	0.006	0.000	6.120	6.122	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	4.246	4.296	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.043	0.008	NaN	0.000	0.000	-1	100	0.894	0.007	0.001	6.225	6.328	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	13.654	13.711	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	NaN	0.001	0.000	1	5	0.911	0.001	0.000	24.996	25.036	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	4.362	4.434	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.003	NaN	0.001	0.000	1	1	0.891	0.001	0.000	36.620	37.005	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	4.560	4.733	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.563	0.076	30	0.028	0.0	random	NaN	0.287	0.015	1.960	1.962	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.624	0.040	30	0.026	0.0	k-means++	NaN	0.334	0.008	1.870	1.871	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.972	0.317	30	0.115	0.0	random	NaN	3.741	0.121	1.864	1.865	See	See
9	KMeans_tall	fit	1000000	1000000	100	7.242	0.106	30	0.110	0.0	k-means++	NaN	3.963	0.044	1.827	1.828	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	random	0.001	0.0	0.0	7.413	9.710	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	12.420	12.483	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.008	0.000	k-means++	0.001	0.0	0.0	9.524	9.836	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	11.025	11.113	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.376	0.000	random	0.002	0.0	0.0	7.563	7.780	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	1.000	0.0	0.0	11.454	11.566	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.385	0.000	k-means++	0.002	0.0	0.0	7.724	7.923	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	1.000	0.0	0.0	13.378	13.443	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.101	0.004	20	0.002	0.0	random	NaN	0.054	0.004	1.876	1.881	See	See
3	KMeans_short	fit	10000	10000	2	0.309	0.006	20	0.001	0.0	k-means++	NaN	0.141	0.003	2.195	2.196	See	See
6	KMeans_short	fit	10000	10000	100	0.337	0.015	20	0.024	0.0	random	NaN	0.264	0.008	1.280	1.281	See	See
9	KMeans_short	fit	10000	10000	100	1.145	0.013	20	0.007	0.0	k-means++	NaN	0.635	0.010	1.802	1.803	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.000	0.001	0.0	3.312	3.327	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.002	20	0.000	0.002	random	1.000	0.000	0.0	14.474	14.569	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	0.001	0.0	3.820	3.831	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	1.000	0.000	0.0	11.974	12.037	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.235	0.000	random	0.279	0.002	0.0	2.247	2.253	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	1.000	0.000	0.0	9.463	9.869	See	See
10	KMeans_short	predict	10000	1000	100	0.004	0.000	20	0.225	0.000	k-means++	0.317	0.002	0.0	2.338	2.340	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	1.000	0.000	0.0	9.562	9.705	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	15.785	0.381	[20]	0.051	0.000	NaN	NaN	NaN	NaN	NaN	2.801	0.038	5.635	5.635	See	See
3	LogisticRegression	fit	1000	1000	10000	1.433	0.602	[26]	0.056	0.001	NaN	NaN	NaN	NaN	NaN	1.087	0.037	1.318	1.319	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.001	[20]	1.386	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.716	1.375	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.010	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.361	0.362	See	See
4	LogisticRegression	predict	1000	100	10000	0.003	0.000	[26]	3.199	0.0	NaN	NaN	NaN	NaN	0.35	0.004	0.001	0.598	0.616	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.618	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.163	0.163	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.292	0.008	NaN	0.274	0.0	NaN	NaN	NaN	0.304	0.009	0.958	0.959	See	See
3	Ridge	fit	1000000	1000000	100	1.279	0.070	NaN	0.626	0.0	NaN	NaN	NaN	0.459	0.208	2.785	3.056	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.011	0.001	NaN	7.616	0.0	NaN	NaN	0.083	0.019	0.001	0.546	0.547	See	See
2	Ridge	predict	1000	1	10000	0.000	0.000	NaN	0.879	0.0	NaN	NaN	NaN	0.000	0.000	0.602	0.614	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.000	NaN	4.613	0.0	NaN	NaN	1.000	0.001	0.001	0.303	0.595	See	See
5	Ridge	predict	1000000	1	100	0.000	0.000	NaN	0.010	0.0	NaN	NaN	NaN	0.000	0.000	0.594	0.614	See	See